Method and system for modifying a digital image taking into account it&#39;s noise

ABSTRACT

A system and a method for calculating a transformed image from a digital image and from formatted information related to defects of an appliance chain for image capture and/or restitution. The system and method provide for automatically determining the characteristic noise data from formatted information and/or from the digital image. The transformed image can therefore be corrected such that it does not exhibit any visible or annoying defects, especially defects related to noise, as regards its subsequent use. The system and method are applicable to photographic or video image processing, in optical devices, industrial controls, robotics, metrology, etc.

FIELD IN QUESTION, PROBLEM POSED

[0001] The present invention relates to a method and a system formodifying a digital image taking its noise into account.

Solution Method

[0002] The invention relates to a method for calculating a transformedimage from a digital image and formatted information related to defectsof an appliance chain. The appliance chain contains image-captureappliances and/or image-restitution appliances. The appliance chaincontains at least one appliance. The method includes the stage ofautomatically determining characteristic data from formatted informationand/or from the digital image. The characteristic data are referred tohereinafter as the characteristic noise data.

[0003] It results from the combination of technical features that thetransformed image does not exhibit any visible or annoying defect,especially defects related to noise, as regards its subsequent use.

Estimation of the Noise as a Function of the Image

[0004] Preferably, according to the invention, the method additionallyincludes the following stages for determining the characteristic noisedata:

[0005] the stage of selecting analysis zones over the digital image,especially as a function of the appliances of the appliance chain and/orof the formatted information,

[0006] the stage of calculating local brightness variations over theanalysis zones,

[0007] the stage of deducing the characteristic noise data as a functionof a statistical calculation of occurrence of local variations over theset of analysis zones.

Estimation of the Noise from the Image Histogram of Brightness Variation

[0008] Preferably, according to the invention, the method additionallyincludes the following stages for deducing the characteristic noisedata:

[0009] the stage of constructing a histogram of occurrences of localbrightness variations,

[0010] the stage of selecting, on the histogram, at least one part ofthe part situated before the first local maximum, including thismaximum.

[0011] It results from the combination of technical features that thelocal brightness variations related to the noise are then obtained.

Estimation of the Noise from the Image Noise as a Function of Brightness

[0012] Preferably, according to the invention, the method additionallyincludes, for selection of analysis zones over the digital image, thestage of classifying the analysis zones according to their meanbrightness, in such a way as to obtain classes. The method additionallyincludes:

[0013] the stage of deducing the characteristic noise data for theanalysis zones belonging to the same class,

[0014] the stage of iterating the preceding stage for each of theclasses.

[0015] It results from the combination of technical features thatcharacteristic noise data as a function of brightness are then obtained.

Formatted Information that Includes Characteristic Noise Data

[0016] Preferably, according to the invention, the formatted informationcontains the characteristic noise data.

Clipping—Problem Posed

[0017] Preferably, according to the invention, the method additionallyincludes the stage of employing a transformation algorithm forconstructing an intermediate digital image. The algorithm has theadvantage of making desired modifications to the digital image butsuffers from the disadvantage of increasing the noise of theintermediate digital image.

Clipping—Solution

[0018] Preferably, according to the invention, to calculate atransformed image from the intermediate digital image obtained from thedigital image, the method additionally includes the stage of employing afunction whose purpose is to modify the brightness of the digital imageand which has, as arguments, at least:

[0019] the brightness of a point of the intermediate digital image,

[0020] the brightnesses of a zone around the corresponding point of thedigital image,

[0021] the characteristic noise data.

[0022] It results from the combination of technical features that thereis then obtained a transformed image exhibiting the desiredcharacteristics and a controlled noise level.

[0023] Preferably, according to the invention, the intermediate digitalimage is composed of the digital image.

Correction of Blurring

[0024] Preferably, according to the invention, the method is moreparticularly designed to calculate a transformed image corrected for allor part of the blurring. The method additionally includes the followingstages:

[0025] the stage of selecting, within the digital image, image zones tobe corrected,

[0026] the stage of constructing, for each image zone to be correctedthat has been selected in this way, an enhancement profile based onformatted information and on characteristic noise data,

[0027] the stage of correcting, as a function of the enhancementprofile, each image zone to be corrected that has been selected in thisway, in such a way as to obtain a transformed image zone,

[0028] the stage of combining the transformed image zones in such a wayas to obtain the transformed image of the digital image.

[0029] It results from the combination of technical features that adeblurred transformed image is then obtained.

Calculation of the Enhancement Profile

[0030] Preferably, according to the invention, the formatted informationmakes it possible to determine, for each image zone to be corrected, animage representation and a reference representation in a base related tothe image zone to be corrected.

[0031] The method is such that, to construct an enhancement profile fromformatted information and noise, it additionally includes the followingstages:

[0032] the stage of determining a profile from the image representationand from the reference representation, while taking the noise intoaccount as the case may be,

[0033] the stage of determining a parameterized operator with which itis possible to pass from the image representation to the profile.

[0034] The set of values of parameters of the parameterized operatorconstitutes the enhancement profile PR.

Correction of Blurring on the Basis of the Enhancement Profile

[0035] Preferably, according to the invention, the method additionallyincludes the following stages for correction of each image zone to becorrected as a function of the enhancement profile:

[0036] the stage of representing at least partly, in the base, the imagezone to be corrected,

[0037] the stage of applying the parameterized operator to therepresentation obtained at the end of the preceding stage, in such a wayas to obtain a corrected representation of the image zone to becorrected,

[0038] the stage of substituting the representation of the image zone tobe corrected by the corrected representation of the image zone to becorrected, in such a way as to obtain a transformed image zone.

Clipping in the Case of Blurring

[0039] Preferably, according to the invention, the method additionallyincludes the stage of calculating, from the transformed image, an imagehaving a controlled noise level, by employing a function whose purposeis to modify the brightness of the digital image and which has, asarguments, at least:

[0040] the brightness of a point of the transformed digital image,

[0041] the brightnesses of a zone around the corresponding point of thedigital image,

[0042] the characteristic noise data.

[0043] It results from the combination of technical features that thereis then obtained a deblurred image having a controlled noise level.

Variable Characteristics Influencing the Noise and/or the Blurring

[0044] The formatted information may depend on values of variablecharacteristic depending on the digital image, especially the size ofthe digital image. Preferably in this case according to the invention,the method additionally includes the stage of determining the value orvalues of the variable characteristics for the digital image.

[0045] Thus employment of the method for formatted information includingcharacteristic noise data that depend on variable characteristicsdepending on the digital image, reduces to employment of the method forcharacteristic noise data that do not depend on any characteristicvariable.

Reduction of the Dynamic Range in the Case of a Restitution Appliance

[0046] Preferably, according to the invention, the method is moreparticularly designed to calculate a transformed image from a digitalimage and from formatted information related to the defects of anappliance chain containing at least one image-restitution appliance. Therestitution appliance has a dynamic range. The transformed image has adynamic range. The method additionally includes the stage of adaptingthe dynamic range of the transformed image to the dynamic range of thesaid restitution appliance. It results from the combination of technicalfeatures that the restitution of the transformed image by therestitution appliance exhibits reinforced high frequencies. It alsoresults from the combination of technical features that the restitutionappliance can restitute images of characters with less blurring.

Correction of Polychromatic Noise and/or Blurring

[0047] The invention is applicable to the case of a digital imagecomposed of color planes. The application comprises applying the methodaccording to the invention to each color plane. In this way atransformed image is obtained from the digital image. It results fromthe combination of technical features that the transformed imageexhibits the desired characteristics and a controlled noise level.

System

[0048] The invention relates to a system for calculating a transformedimage from a digital image and formatted information related to defectsof an appliance chain. The appliance chain contains image-captureappliances and/or image-restitution appliances. The appliance chaincontains at least one appliance. The system includes data-processingmeans for automatically determining characteristic data from formattedinformation and/or from the digital image. The characteristic data arereferred to hereinafter as the characteristic noise data.

[0049] The transformed image does not exhibit any visible or annoyingdefect, especially defects related to noise, as regards its subsequentuse.

Estimation of the Noise as a Function of the Image

[0050] Preferably, according to the invention, the data-processing meansfor determining the characteristic noise data include:

[0051] selection means for selecting analysis zones over the digitalimage, especially as a function of the appliance of the appliance chainand/or of the formatted information,

[0052] calculating means for calculating local brightness variationsover the analysis zones,

[0053] deducing means for deducing the characteristic noise data as afunction of a statistical calculation of occurrence of local variationsover the set of analysis zones.

Estimation of the Noise from the Image Histogram of Brightness Variation

[0054] Preferably, according to the invention, the deducing meansadditionally include:

[0055] means for constructing a histogram of occurrences of localbrightness variations,

[0056] selection means for selecting, on the histogram, at least onepart of the part situated before the first local maximum, including thismaximum.

Estimation of the Noise from the Image Noise as a Function of Brightness

[0057] Preferably, according to the invention, the system additionallyincludes, for selection of analysis zones over the digital image,classification means for classifying the analysis zones according totheir mean brightness, in such a way as to obtain classes. The systemadditionally includes data-processing means for:

[0058] deducing the characteristic noise data for the analysis zonesbelonging to the same class,

[0059] iterating the preceding stage for each of the classes.

Formatted Information that Includes Characteristic Noise Data

[0060] Preferably, according to the invention, the formatted informationcontains the characteristic noise data.

Clipping—Problem Posed

[0061] Preferably, according to the invention, the system additionallyincludes data-processing means employing a transformation algorithm forconstructing an intermediate digital image. The algorithm has theadvantage of making desired modifications to the digital image butsuffers from the disadvantage of increasing the noise of theintermediate digital image.

Clipping—Solution

[0062] Preferably, according to the invention, to calculate atransformed image from the intermediate digital image obtained from thedigital image, the system includes calculating means employing afunction whose purpose is to modify the brightness of the digital imageand which has, as arguments, at least:

[0063] the brightness of a point of the intermediate digital image,

[0064] the brightnesses of a zone around the corresponding point of thedigital image,

[0065] the characteristic noise data.

[0066] Preferably, according to the invention, the intermediate digitalimage is composed of the digital image.

Correction of Blurring

[0067] Preferably, according to the invention, the system is moreparticularly designed to calculate a transformed image corrected for allor part of the blurring. The system additionally includes:

[0068] selection means for selecting, within the digital image, imagezones to be corrected,

[0069] calculating means for constructing, for each image zone to becorrected that has been selected in this way, an enhancement profilebased on formatted information and on characteristic noise data.

[0070] The system additionally includes data-processing means for:

[0071] correcting, as a function of the enhancement profile, each imagezone to be corrected that has been selected in this way, in such a wayas to obtain a transformed image zone, and for

[0072] combining the transformed image zones in such a way as to obtainthe transformed image of the digital image.

Calculation of the Enhancement Profile

[0073] Preferably, according to the invention, the formatted informationmakes it possible to determine, for each image zone to be corrected, animage representation and a reference representation in a base related tothe image zone to be corrected. The system is such that the calculatingmeans for constructing an enhancement profile from formatted informationand noise additionally include means for determining:

[0074] a profile from the image representation and from the referencerepresentation, while taking the noise into account as the case may be,

[0075] a parameterized operator with which it is possible to pass fromthe image representation to the profile.

Correction of Blurring on the Basis of the Enhancement Profile

[0076] Preferably, according to the invention, the data-processing meansfor correction of each image zone to be corrected as a function of theenhancement profile include calculating means for:

[0077] representing at least partly, in the base, the image zone to becorrected,

[0078] applying the parameterized operator to the representation of theimage zone to be corrected, in such a way as to obtain a correctedrepresentation of the image zone to be corrected,

[0079] substituting the representation of the image zone to be correctedby the corrected representation of the image zone to be corrected, insuch a way as to obtain a transformed image zone.

Clipping in the Case of Blurring

[0080] Preferably, according to the invention, the system additionallyincludes calculating means for calculating, from the transformed image,an image having a controlled noise level, by employing a function whosepurpose is to modify the brightness of the digital image and which has,as arguments, at least:

[0081] the brightness of a point of the transformed digital image,

[0082] the brightnesses of a zone around the corresponding point of thedigital image,

[0083] the characteristic noise data.

Variable Characteristics Influencing the Noise and/or the Blurring

[0084] Preferably, according to the invention, the formatted informationdepends on values of variable characteristics depending on the digitalimage, especially the size of the digital image. The system additionallyincludes calculating means for determining the value or values of thevariable characteristics for the digital image.

Reduction of the Dynamic Range in the Case of a Restitution Appliance

[0085] Preferably, according to the invention, the system is moreparticularly designed to calculate a transformed image from a digitalimage and from formatted information related to the defects of anappliance chain containing at least one image-restitution appliance. Therestitution appliance has a dynamic range. The transformed image has adynamic range. The system additionally includes data-processing meansfor adapting the dynamic range of the transformed image to the dynamicrange of the restitution appliance.

DETAILED DESCRIPTION

[0086] Other characteristics and advantages of the invention will becomeapparent upon reading of the description of alternative embodiments ofthe invention, provided by way of indicative and non-limitativeexamples, and of:

[0087]FIG. 1, which illustrates a transformed image calculated from adigital image and an intermediate image,

[0088]FIG. 2, which illustrates defects of the digital image,

[0089]FIG. 3, which illustrates a selection of analysis zones over thedigital image,

[0090]FIG. 4a, which illustrates a local brightness variation over ananalysis zone,

[0091]FIG. 4b, which illustrates a histogram of occurrences of localbrightness variations, before the first local maximum of the histogram,

[0092]FIG. 5, which illustrates classes of analysis zones according totheir mean brightness,

[0093]FIG. 6, which illustrates the modification of brightness of thedigital image,

[0094]FIG. 7a, which illustrates the correction of a transformed imagezone as a function of an enhancement profile,

[0095]FIG. 7b, which illustrates an example of creation of a deblurredimage with controlled noise level,

[0096]FIGS. 8a and 8 b, which illustrate the construction of anenhancement profile from the noise,

[0097]FIGS. 9a, 9 b, 9 c and 9 d, which present the adaptation of thedynamic range of the transformed image to the dynamic range of arestitution appliance,

[0098]FIG. 10: formatted information IF related to the defects P5 of anappliance P25 of an appliance chain P3.

APPLIANCE

[0099] Referring in particular to FIG. 10, a description will be givenof the concept of appliance P25. Within the meaning of the invention, anappliance P25 may be in particular:

[0100] an image-capture appliance, such as a disposable photo appliance,a digital photo appliance, a reflex appliance, a scanner, a fax machine,an endoscope, a camcorder, a surveillance camera, a webcam, a cameraintegrated into or connected to a telephone, to a personal digitalassistant or to a computer, a thermal camera or an echographicappliance,

[0101] an image-restitution appliance, such as a screen, a projector, atelevision set, virtual-reality goggles or a printer,

[0102] a human being having vision defects, such as astigmatism,

[0103] an appliance which it is hoped can be emulated, to produce imageshaving, for example, an appearance similar to those produced by anappliance of the Leica brand,

[0104] an image-processing device, such as zoom software, which has theedge effect of adding blurring,

[0105] a virtual appliance equivalent to a plurality of appliances P25,

[0106] A more complex appliance P25, such as a scanner/fax/printer, aphoto-printing. Minilab, or a video conferencing appliance can beregarded as an appliance P25 or as a plurality of appliances P25.

Appliance Chain

[0107] Referring in particular to FIG. 10, a description will now begiven of the concept of appliance chain P3. An appliance chain P3 isdefined as a set of appliances P25. The concept of appliance chain P3may also include a concept of order.

[0108] The following examples constitute appliance chains P3:

[0109] a single appliance P25,

[0110] an image-capture appliance and an image-restitution appliance,

[0111] a photo appliance, a scanner or a printer, for example in aphoto-printing Minilab,

[0112] a digital photo appliance or a printer, for example in aphoto-printing Minilab,

[0113] a scanner, a screen or a printer, for example in a computer,

[0114] a screen or projector, and the eye of a human being,

[0115] one appliance and another appliance which it is hoped can beemulated,

[0116] a photo appliance and a scanner,

[0117] an image-capture appliance and image-processing software,

[0118] image-processing software and an image-restitution appliance,

[0119] a combination of the preceding examples,

[0120] another set of appliances P25.

Defect

[0121] Referring in particular to FIG. 10, a description will now begiven of the concept of defect P5. A defect P5 of appliance P25 isdefined as a defect related to the characteristics of the optical systemand/or of the sensor and/or of the electronic unit and/or of thesoftware integrated in an appliance P25; examples of defects P5 includedistortion, blurring, vignetting, chromatic aberrations, colorrendering, flash uniformity, sensor noise, grain, astigmatism andspherical aberration.

Digital Image

[0122] Referring in particular to FIG. 10, a description will now begiven of the concept of digital image INUM. A digital image INUM isdefined as an image captured or modified or restituted by an applianceP25. Digital image INUM may be derived from an appliance P25 ofappliance chain P3. Digital image INUM may be addressed to an applianceP25 of appliance chain P3. More generally, digital image INUM may bederived from and/or addressed to appliance chain P3. In the case ofanimated images, such as video images composed of a time sequence offixed images, digital image INUM is defined as a fixed image of theimage sequence.

Formatted Information

[0123] Referring in particular to FIG. 10, a description will now begiven of the concept of formatted information IF. Formatted informationIF is defined as data related to the defects P5 of one or moreappliances P25 of appliance chain P3 and enabling transformed imageI-Transf to be calculated by taking into account the defects P5 ofappliance P25. The formatted information IF can be produced by usingvarious methods based on measurements, and/or on captures or restitutionof references, and/or on simulations.

[0124] To produce the formatted information IF, it is possible, forexample, to use the method described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for producing formattedinformation related to defects of at least one appliance of a chain, inparticular blurring”. That application describes a method for producingformatted information related to the appliances of an appliance chain.The appliance chain is composed in particular of at least oneimage-capture appliance and/or at least one image-restitution appliance.The method includes the stage of producing formatted information relatedto the defects of at least one appliance of the chain. The appliancepreferably makes it possible to capture or restitute an image (I). Theappliance contains at least one fixed characteristic and/or one variablecharacteristic depending on the image (I). The fixed and/or variablecharacteristics can be associated with one or more values ofcharacteristics, especially the focal length and/or the focusing andtheir values of associated characteristics. The method includes thestage of producing, from a measured field D(H) measured formattedinformation related to the defects of the appliance. The formattedinformation may include the measured formatted information.

[0125] To produce the formatted information IF, it is possible, forexample, to use the method described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for providing formattedinformation in a standard format to image-processing means”. Thatapplication describes a method for providing formatted information IF ina standard format to image-processing means, especially software and/orcomponents. The formatted information IF is related to the defects of anappliance chain P3. The appliance chain P3 includes in particular atleast one image-capture appliance and/or one image-restitutionappliance. The image-processing means use the formatted information IFto modify the quality of at least one image derived from or addressed tothe appliance chain P3. The formatted information IF includes datacharacterizing the defects P5 of the image-capture appliance, especiallythe distortion characteristics, and/or data characterizing the defectsof the image-restitution appliance, especially the distortioncharacteristics.

[0126] The method includes the stage of filling in at least one field ofthe said standard format with the formatted information IF. The field isdesignated by a field name. The field contains at least one field value.

[0127] To search for the formatted information IF, it is possible, forexample, to use the method described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for modifying the qualityof at least one image derived from or addressed to an appliance chain”.That application describes a method for modifying the quality of atleast one image derived from or addressed to a specified appliancechain. The specified appliance chain is composed of at least oneimage-capture appliance and/or at least one image-restitution appliance.The image-capture appliances and/or the image-restitution appliancesbeing progressively introduced on the market by separate economicplayers belong to an indeterminate set of appliances. The appliances ofthe set of appliances exhibit defects that can be characterized byformatted information. For the image in question, the method includesthe following stages:

[0128] the stage of compiling directories of the sources of formattedinformation related to the appliances of the set of appliances,

[0129] the stage of automatically searching for specific formattedinformation related to the specified appliance chain among the formattedinformation compiled in this way,

[0130] the stage of automatically modifying the image by means ofimage-processing software and/or image-processing components, whiletaking into account the specific formatted information obtained in thisway.

[0131] To produce the formatted information IF, it is possible, forexample, to use the method described in the International PatentApplication filed on the same day as the present application in the nameof Vision IQ and entitled “Method and system for reducing updatefrequency of image processing means”. That application describes amethod for reducing the update frequency of image-processing means, inparticular software and/or a component. The image-processing means makeit possible to modify the quality of the digital images derived from oraddressed to an appliance chain. The appliance chain is composed inparticular of at least one image-capture appliance and/or at least oneimage-restitution appliance. The image-processing means employ formattedinformation related to the defects of at least one appliance of theappliance chain. The formatted information depends on at least onevariable. The formatted information makes it possible to establish acorrespondence between one part of the variables and of the identifiers.By means of the identifiers it is possible to determine the value of,the variable corresponding to the identifier by taking the identifierand the image into account. It results from the combination of technicalfeatures that it is possible to determine the value of a variable,especially in the case in which the physical significance and/or thecontent of the variable are known only after distribution ofimage-processing means. It also results from the combination oftechnical features that the time between two updates of the correctionsoftware can be spaced apart. It also results from the combination oftechnical features that the various economic players that produceappliances and/or image-processing means can update their productsindependently of other economic players, even if the latter radicallychange the characteristics of their product or are unable to force theirclient to update their products. It also results from the combination oftechnical features that a new functionality can be deployedprogressively by starting with a limited number of economic players andpioneer users.

Variable Characteristic

[0132] A description will now be given of the concept of variablecharacteristic CC. According to the invention, a variable characteristicCC is defined as a measurable factor, which is variable from one digitalimage INUM to another that has been captured, modified or restituted bythe same appliance P25, and which has an influence on defect P5 of theimage that has been captured, modified or restituted by appliance P25,especially:

[0133] a global variable, which is fixed for a given digital image INUM,an example being a characteristic of appliance P25 at the moment ofcapture or restitution of the image, related to an adjustment of theuser or related to an automatic function of appliance P25,

[0134] a local variable, which is variable within a given digital imageTNLM, an example being coordinates x, y or rho, theta in the image,making it possible to apply, as need be, local processing that differsdepending on the zone of the digital image INUM.

[0135] A measurable factor which is variable from one appliance P25 toanother but which is fixed from one digital image INUM to another thathas been captured, modified or restituted by the same appliance P25, isnot generally considered to be a variable characteristic CC; an exampleis the focal length for an appliance P25 with fixed focal length.

[0136] The formatted information IF may depend on at least one variablecharacteristic CC.

[0137] By variable characteristic CC there can be understood inparticular:

[0138] the focal length of the optical system,

[0139] the redimensioning applied to the image (digital zoom factor:enlargement of part of the image; and/or under-sampling: reduction ofthe number of pixels of the image),

[0140] the nonlinear brightness correction, such as the gammacorrection,

[0141] the enhancement of contour, such as the level of deblurringapplied by appliance P25,

[0142] the noise of the sensor and of the electronic unit,

[0143] the aperture of the optical system,

[0144] the focusing distance,

[0145] the number of the frame on a film,

[0146] the underexposure or overexposure,

[0147] the sensitivity of the film or sensor,

[0148] the type of paper used in a printer,

[0149] the position of the center of the sensor in the image,

[0150] the rotation of the image relative to the sensor,

[0151] the position of a projector relative to the screen,

[0152] the white balance used,

[0153] the activation of a flash and/or its power,

[0154] the exposure time,

[0155] the sensor gain,

[0156] the compression,

[0157] the contrast,

[0158] another adjustment applied by the user of appliance P25, such asa mode of operation,

[0159] another automatic adjustment of appliance P25,

[0160] another measurement performed by appliance P25.

Variable Characteristic Value

[0161] A description will now be given of the concept of variablecharacteristic value VCC. A variable characteristic value VCC is definedas the value of variable characteristic CC at the moment of capture,modification or restitution of a specified image.

Calculation of the Transformed Image

[0162] A digital image INUM contains a set of image elements defined aspixels PX-num.1 to PX-num.n distributed regularly over the surface ofimage INUM. In FIG. 1, these pixels have the shape of squares, but theycould also have any other shape, such as circles or hexagons; thatdepends on the style of the surfaces designed to carry the image in theappliances for image capture and restitution. In addition, in FIG. 1 thepixels were illustrated as being joined, but in reality some spacing isgenerally present between the pixels. The associated brightness at anarbitrary point Px-num is vx-num.

[0163] Intermediate image I-Int contains a set of pixels similar to thatof image INUM, but not necessarily so, defined as intermediate pixelsPx-int.1 to Px-int.n. Each intermediate pixel is characterized by anintermediate position Px-int and an intermediate value vx-int.

[0164] Transformed image I-Transf also contains a set of pixels definedas transformed pixels PX-tr.1 to PX-tr.n. Each transformed pixel ischaracterized by a transformed position Px-tr and a transformed valuevx-tr.

[0165] A transformed image is a corrected or modified image obtained byapplication of a transformation to an image INUM. This transformation,which may be a photometric transformation, is performed byincorporating, in the calculation,

[0166] image INUM,

[0167] characteristic noise data DcB in INUM

[0168] image I-Int

[0169] formatted information that takes into account, for example,defects of the appliances used and/or characteristics that are to beintroduced into the image.

[0170] It will be noted that the formatted information may be related toa limited number of transformed pixels and/or may incorporate values ofvariable characteristics that depend on the image (such as the focallength, the focusing, the aperture, etc.). In this case there may exista supplementary stage which, for example, is performed by interpolationin such a way that it is reduced to simple formatted information such asthat of an appliance that is not provided with any variablecharacteristics, so that the case of appliances with variable focallength in particular reduces to the case of an appliance with fixedfocal length.

[0171] It will be noted that the formatted information can be related toa limited number of transformed pixels and/or of values of variablecharacteristics depending on the image. In this case it is possible toinclude a supplementary stage, which is performed, for example, byinterpolation. In the example of a function x′,y′=f(x, y, t), where t isa variable characteristic (such as focal length), the formattedinformation can be composed of a limited number of values (xi, yi, ti,f(xi, yi, ti)). It is then necessary to calculate an approximation forthe other values of x, y, t other than the measurement points. Thisapproximation can be applied by resorting to simple interpolationtechniques or by using parameterizable models (polynomials, splines,Bezier functions) having greater or lesser order depending on thedesired final precision. With an analogous formalism, t could be avector and could include a plurality of variable characteristics (focallength, focusing, zoom, etc.) simultaneously.

[0172] In the case of noise and/or of blurring, the formattedinformation could be composed if necessary of vectors with which thenoise and/or the blurring related to an appliance and/or to an appliancechain can be characterized, for the set of combinations of variableparameters of the device, especially by resorting to characteristicprofiles of the defect in special representation bases, especiallyfrequency representations such as Fourier transforms, wavelettransforms, etc. In fact, the person skilled in the art knows thatfrequency representations are compact domains that are appropriate forrepresentation of physical phenomena related to noise and/or toblurring.

[0173] It is additionally possible to combine the formatted informationIF related to a plurality of appliances P25 of an appliance chain P3 toobtain formatted information related to a virtual appliance exhibitingthe defects of the said plurality of appliances P25; in such a way thatit is possible to calculate, in one stage, transformed image I-Transffrom image INUM for all of the said plurality of appliances P25; in sucha way that the said calculation is faster than if the method accordingto the invention were to be applied successively to each appliance P25;in the example of a frequency representation such as the Fouriertransform, the said combination can be achieved cumulatively from thecharacteristic profiles of the defect of each appliance, for example bymultiplication.

[0174] It will be possible for the formatted information to include notonly data that were studied in a preliminary phase and that are relatedto the appliances used, but also all information styled in the Exif orother format that could provide particulars on the adjustments of theappliance at the moment of filming (focal length, focusing, aperture,speed, flash, etc.).

[0175] Let us assume that digital image INUM represents, for example,capture of the monochromatic image of a white square on a blackbackground. FIG. 2 illustrates the brightnesses of a line of image INUM.Because of the noise and/or blurring generated by the capture and/orrestitution chain, the ideal profile (a step of a staircase) isdeformed. The method of the invention makes it possible, by means ofcalculating means CAPP that incorporate approximations according to adesired final precision, among other factors, to obtain, on transformedimage I-Transf, a square in which the brightness value vx-tr at each ofthe points px-tr is effectively corrected to within the approximations.

[0176] We note that the application of algorithm CAPP may, in the caseof noise and/or blurring, reduce original image INUM to a perfect orquasi-perfect image. The same algorithm may also reduce image INUM toanother image, which may be deformed, albeit differently, in such a wayas to produce an image; closely resembling a known type of image noiseand/or blurring (retro noise effect, etc.). The same method also makesit possible to reduce image INUM to an image that is not perfect (in thesense of a white square on a black background, as in FIG. 2) but isoptimal to the eyes of the observer, such that it is possible tocompensate if necessary for defects of perception of the human eye.

Estimation of the Noise

[0177] For certain types of appliance APP, especially for image capture,it is possible to deduce characteristic noise data DcB from formattedinformation. For example, this is the case in particular for applianceswith which it is possible to determine particulars of variablecharacteristics that influence noise, such as gain, ISO, etc. Thedependence between noise and these characteristics will be entered intothe formatted information, especially by means of polynomial functions.

[0178] To the extent that characteristic noise data cannot be deduceddirectly or indirectly from formatted information, it will be necessaryto deduce these characteristic data. We will therefore describe, withinthe meaning of the present invention, a practical example with whichthere can be produced characteristic noise data DcB that are related toan image INUM.

[0179] Image INUM is subdivided into a series of analysis zones (ZAN),which are not necessarily joined and which may intersect as the case maybe. FIG. 3 illustrates an example of subdivision. An analysis zone ZANmay have an arbitrary shape, and it is not absolutely necessary toanalyze all of the points inscribed in the said analysis zone ZAN. Foreach analysis zone ZAN, such as a square window with a size of 3×3 or5×5 pixels, the method undertakes a measurement of local brightnessvariation (VLL). The set of measurements of local brightness variationsfor all analysis zones ZAN is then analyzed statistically to produce oneor more characteristic noise data DcB related to image INUM.

[0180] An example of measurement of local brightness variation VLL canbe achieved by calculating, over an analysis zone ZAN, the maximumbrightness deviation among the set of points. In FIG. 4a, VLL has avalue of 29, which represents the maximum deviation between two pixelsof the zone. Another way could be to calculate the standard deviation ofthe distribution relative to the brightness variation.

[0181] It is possible to analyze the set of measurements of localbrightness variation VLL statistically by creating a histogram offrequencies of occurrence of the variations. In such a histogram, anexample of which is illustrated in FIG. 4b, the abscissa represents aquantization of the brightness deviations *VLL according to theprecision of noise measurement. The ordinate represents the total numberof occurrences of an analysis zone ZAN giving the value VLL. In theexample, there were 22 analysis zones ZAN for which the measurement oflocal brightness variation gave the value 50.

[0182] The profile of this histogram for a natural image, such as alandscape image containing a random distribution of patterns ofdifferent brightness but having homogeneous brightness over smallanalysis zones, contains a characteristic zone situated before the firstlocal maximum (FIGS. 4b, 4 c). Assuming that a natural image contains alarge number of zones of reduced size (size of an analysis zone ZAN) forwhich the illumination is quasi-uniform, then the first local maximum ofthe histogram (with abscissa xm and ordinate fm) characterizes the meannoise of image INUM. For the image with very little noise, we will havemany measurements VLL exhibiting small brightness deviations, and theabscissa of the first mode will be close to the origin; in contrast, ifthe image incorporates much noise derived from different appliances ofthe chain, each measurement VLL performed on theoretically homogeneouszones will generate high values and will shift the abscissa of the firstmode of the histogram away from the origin.

[0183] The characteristic noise data of image INUM may be composed ofthe set of values of the histogram up to the first mode. Another way ofextracting more synthetic information from the noise characteristiccomprises, as illustrated in FIG. 4c, defining a mean noise value BM asbeing that abscissa xb, located between the origin and the first mode ofthe histogram (xm), for which the ordinate is a fraction (typicallyhalf) of fm.

[0184]FIG. 5 illustrates an alternative version of the calculation ofcharacteristic noise data DcB. According to an analogous procedure foranalysis of analysis zones ZAN, the invention provides forsimultaneously estimating, at the local brightness variation VLL,information related to the mean brightness in the said analysis zone ZAN(for example, the algebraic mean of the brightnesses over the zone). Themethod also provides, as a function of the quantization of imagebrightness, for creation of classes that subdivide the brightness scalelinearly or nonlinearly. For quantization on 8 bits, the maximum numberof classes is 255; typically we will use between 5 and 10 classes (C1 .. . Cn) of brightness subdivision. In a practical example of the method,it will be possible for the choice of subdivision to be a function ofthe brightness histogram of image INUM. To each class there willcorrespond a histogram of cumulative frequency of occurrence of a VLL,in such a way that the noise contained in image INUM is analyzed bybrightness interval.

[0185] In FIG. 5 we described three examples of analysis zone Z,N for ananalysis of noise characteristics in three classes. For zone ZAN-i, themean brightness is 5.8, and so this zone belongs to class C1 and themeasurement of VLL (which is equal to 11) will therefore be accumulatedin histogram HCl related to C1. An analogous procedure is carried outfor analysis zones ZAN-j and ZAN-p which, in view of their measurementof mean brightness, belong respectively to classes C2 and C3. When allanalysis zones ZAN constituting image INUM have been analyzed, we willhave as many histograms as there are classes. By analogy with theforegoing description, it is possible to extract one characteristicvalue of noise per histogram and therefore per class, and thus tocompose a set of characteristic noise data DcB=[(C1, BM1), (C2, BM2), .. . , (Cn, BMn)] of INUM.

Clipping

[0186] Let us consider a digital image INUM within the meaning of thepresent invention, and let us also consider a transformation applicableto an INUM in such a way as to construct an intermediate image which, incertain respects, has the advantage of making the desired modificationsbut, on the other hand, suffers from the disadvantage of increasing theimage noise in certain zones. As we will see hereinafter, it will bepossible for this transformation to be a transformation that reducesblurring, a transformation that increases contrast, a transformationwith which image mosaics can be created, or any other transformationcapable of modifying the noise characteristics between image INUM andI-Int. The method illustrated in FIG. 6 is defined as clipping, which isto be understood within the scope of the invention as the taking ofportions of images. Calculation of the brightness vx-tr of a transformedpixel Px-tr-j requires information related to:

[0187] pixel Px-num-j and an analysis zone ZAN-j around the point,

[0188] pixel Px-int-j,

[0189] characteristic noise data DcB.

[0190] The analysis of the mean brightness and of the local brightnessvariation VLL in zone ZAN-j makes it possible to determine class Cj towhich the noise belongs and to extract the data DcB of noise BM-j.According to one option, it is possible to calculate a normalized ratioRj between BM-j and VLL. As illustrated in FIG. 6, if Rj tends to 1(case in which the local brightness variation VLL is substantially onthe same order as BM-j, and so what is being measured is the noise),then the brightness vx-tr of transformed pixel Px-tr-j is taken for themost part in INUM. The brightness value of a transformed pixel can thenbe expressed as a function of pixel brightness vx-num, of pixelbrightness vx-int and of the characteristic noise data. One special casemay be the following rule:

vx-tr=(Rj)vx-num+(1−Rj)vx-int

[0191] where vx-num and vx-int represent the brightnesses of Px-num-jand Px-int-j respectively. In the opposite case (the local brightnessvariation VLL is large compared with BM-j, thus corresponding to thesignal), the ratio Rj tends to 0 and the brightness vx-tr of transformedpixel Px-tr-j is taken for the most part in intermediate image I-Int.

[0192] More generally, the brightness value of a transformed pixel canbe expressed as a function of the brightnesses of pixel vx-num and itsneighbors, of the brightnesses of pixel vx-int and its neighbors andfinally of the characteristic noise data.

[0193] It will therefore be possible, for example, to deduce thetransformed image from the intermediate image by applying a more or lessintensive filtering operation in the latter on the basis of noisemeasured in INUM.

[0194] This method has the advantage that it takes, in the intermediateimage, only information that is pertinent to the exclusion of points forwhich the noise analyzed in original image INUM is too large within themeaning of a global statistical study of noise characterized by the dataDcB.

[0195] It is understood that it is possible, during the clippingoperation, to apply any relation, especially linear or nonlineartransformations, for passage between the images INUM and I-Int.

[0196] In FIG. 3, the system according to the invention contains adevice SZ for selection of analysis zones. In FIG. 6, it contains acalculating device MC1 for calculating an intermediate pixel from apixel Pi of image INUM. Furthermore, a calculating device dcb makes itpossible to calculate the characteristic noise data DcB and to provide acoefficient Rj. By means of calculating device MC2 it is possible tocalculate the value of a transformed pixel, or in other words itsbrightness, from the values of digital and corresponding intermediatepixels and from coefficient Rj.

Correction of Blurring

[0197] We will now describe a practical example of a method designedmore particularly to calculate a transformed image corrected for all orpart of the blurring. The description of this method is based on thepractical example of the system shown in FIG. 7a. Digital image INUM issubdivided into image zones ZIC to be corrected. The set of these zonescovers the entirety of image INUM and, as the case may be, it ispossible for these zones to overlap if necessary in order to reducecertain perturbing effects better known to the person skilled in the artby the term edge effect. The creation of a transformed image zone ZIC*corrected for the blurring defect employs a process that requires thefollowing parameters, but is not limited thereto, as argument:

[0198] knowledge, at the moment of filming, of values of variableparameters of the appliance or of the appliance chain for image captureand/or restitution,

[0199] the brightness at each point Px-num belonging to zone ZIC,

[0200] the characteristic noise data DcB of INUM,

[0201] the formatted information related to modeling of the blurring ofthe appliance and/or of the appliance chain, if necessary modeledbeforehand by means of a parameterizable model.

[0202] For a configuration of given arguments (focal length, focusing,zoom, aperture, etc., DcB, zone ZIC), it is possible, by means of theparameterizable model of formatted information, to access characteristicblurring profiles related to an image representation RI and a referencerepresentation RR. These profiles are expressed in a particular base,especially a frequency base B, by using, for example, a Fouriertransform, a wavelet transform, etc.

[0203] Base B will be implicit or else will be established within theformatted information. Within the meaning of the present invention, theperson skilled in the art sees that it is possible to represent adigital image (such as INUM) in a vector space of dimension equal to thenumber of pixels. By base B there is understood, non-exclusively, abase, in the mathematical sense of the term, of this vector space and/ora vector subspace thereof.

[0204] Hereinafter, frequency is defined as an identifier related toeach element of the base. The person skilled in the art understandsFourier transformations and/or wavelet transforms as changes of the baseof the image space. In the case of an appliance APP for which theblurring defects significantly affect only a subspace of the vectorspace of the images, it will be necessary to correct only thosecomponents of image INUM that belong to this subspace. Thus base B willbe chosen preferably as a base for representation of this subspace.

[0205] Another way of employing the method within the meaning of theinvention is to choose, for representation of the image, a base that isoptimal within the meaning, for example, of that of calculation time. Itwill be possible to choose this base with small dimension, each elementof the base having a support of a few pixels spatially localized inimage INUM (for example, splines or sets of Laplace operators of localvariations, Laplacian of Laplacian, or derivatives of higher order,etc.).

[0206] The measurement of the local brightness variation VLL over zoneZIC makes it possible, by virtue of the characteristic noise data DcB ofINUM, to calculate a coefficient Rj (device dcb2) This coefficient willbe coupled with the representations RI and RR (device pr) to generate afrequency-based enhancement profile PR related to zone ZIC. This profileindicates the gain to be applied at each frequency related to thebrightness information contained in zone ZIC to be corrected, in orderto suppress all or part of the blurring.

[0207]FIG. 7a shows that it is then sufficient to express zone ZIC in abase B, especially an adequate frequency base B(ZIC), to apply theenhancement function for all or part of the frequencies.B(ZIC*)=B(ZIC)*PR, and then, by an inverse transform, to find thetransformed image zone. The set of transformed image zones is thencombined in such a way as to obtain the deblurred transformed image(I-Transf ID). By means of this combination it is possible, for example,to apply solutions in the case of overlap of ZIC, especially to limitthe edge effects.

[0208] Creation of the image (I-Transf ID) as described in the foregoinghas the advantage of applying the necessary modifications to image INUMfrom the viewpoint of blurring, but suffers from the disadvantage ofincreasing the noise in certain zones (especially relatively uniformzones).

[0209] A second employment of a method of the present invention is basedon the practical example of the system shown in FIG. 7b. It makes itpossible to construct a deblurred image (I-Trans IDBC) having acontrolled noise level. Creation of the transformed image (I-TransfIDBC) employs a clipping procedure similar to that described hereinabovein FIG. 6, by means of device dcb1 and of the clipping device. In thispresent case, the intermediate image as defined in FIG. 6 is nothingother than the deblurred image (I-Trans ID).

[0210]FIG. 8 describes more precisely the production of enhancementprofile PR for a specified zone ZIC. The image representation RI andreference representation RR that have been extracted from formatted dataand that are related to an image zone ZIC to be corrected arecharacteristic of the blurring introduced by the acquisition and/orrestitution system for a given configuration of variable parameters atthe moment of filming (focal length 10 mm, focusing at infinity,aperture f/2, etc.). These representations RR and RI express thefollowing concepts:

[0211] RI is the frequency profile of a zone ZIC of a reference scene asgenerated by the device and containing blurring,

[0212] RR is the optimal frequency profile of the same zone ZIC as itwould have been generated if the device had not generated blurring.

[0213] We see that the ratio between these two profiles can indicate thegain for each frequency to be applied to RI to find RR. On the otherhand; it turns out that direct application of the calculated gainbetween RI and RR can generate undesirable behaviors, especially withhigh frequencies, when zone ZIC to be corrected contains a high noiselevel. These phenomena are known by the person skilled in the art as theeffect of brightness oscillations defined as “ringing”. According to theinvention, the method will estimate, between RR and RI, a profile whoseposition is parameterized as a function of the noise in analyzed zoneZIC.

[0214]FIGS. 8a and 8 b show two examples of profiles PR that can begenerated according to the invention. The deviation between profiles RIand RR shows the frequency loss introduced by the blurring inherent tothe device.

[0215]FIG. 8a treats the case of high noise level in zone ZIC; it willbe advisable to choose, between RI and RR, a profile RH such that itseffect is less at the high frequencies (the end of RH will coincide withRI), which in this case carry the information related to the noise inthe image.

[0216] In contrast, FIG. 8b treats the case of very low noise level inzone ZIC; the high frequencies of profile RI therefore represent thesignal and no longer the noise. It will then be advisable to choose,between RI and RR, a profile. RH such that the gain between RH and RIremains large even at the high frequencies, in order to reinforce theperception of details in zone ZIC.

[0217] In any case, RH will not be permitted to exceed RR, which is theideal profile of the device but does not correspond to an image that canbe constructed by a real device. In view of the foregoing description,it is possible to choose multiple functions that parameterize a curve ofprofile RH between RI and. RR. In FIGS. 8a, 8 b, the representation basechosen for representations RR and RI is the Fourier base. The abscissarepresents the signal frequencies and the ordinate represents thelogarithm of the modulus of the Fourier transform. One approach inparticular to calculation of a representation of profile. RH is toremain tangential to profile RR at low frequency and then (FIGS. 8a, 8b) to follow a straight line up to the extreme point characterizing thehigh frequencies.

[0218] Construction of the frequency-based enhancement profile PR isperformed immediately by calculation of the ratio RH/RI for allfrequencies.

Correction of Polychromatic Noise and/or Blurring

[0219] The method of the invention is applicable to the processing ofcolor images. From the viewpoint of image-processing software, a colorimage is considered to contain as many images (or color planes) as thereare basic colors in the image. Thus an image IMrgb is considered to becomposed of the three color planes Im-red, Im-green, Im-blue.Analogously, an image IMcmyk may be considered to be composed of fourcolor planes: Im-cyan, Im-magenta, Im-yellow, Im-black. In the methoddescribed in the foregoing, each color plane will be processedindependently in such a way as to obtain n transformed images, whichwill recompose the different color planes of the transformed finalimage.

Reduction of the Dynamic Range Upstream from a Restitution Appliance

[0220] The method of the invention is applicable to calculation of atransformed digital image I-Transf designed to be displayed via arestitution means of known dynamic range (FIG. 9a) in order to create animage I-REST. This restitution means, such as a projector, intrinsicallyintroduces blurring at the moment of restitution, and this may bemanifested in FIG. 9b, for example, by attenuation of the profile of atransition in the form of a staircase step. In order to obtain a morefavorable restitution, it will be advisable (FIG. 9c) to modify thedynamic range of the transformed image upstream in such a way that theprojected image will have a profile closer to the ideal profile. Thismodification of dynamic range is not always feasible, because of thequantization of the transformed image (generally in 8 bits). Toalleviate this difficulty, the method can reduce the global dynamicrange of the transformed image (the image becomes less contrasted, andtherefore less energetic). It is possible to apply thereto thetransformations necessary to take into account the blurring of therestitution appliance, while remaining within the permissible dynamicrange of the image (FIG. 9c) and, in the case of a lamp-type projectorappliance, to compensate for the energy drop at the level of therestitution appliance itself, which no longer has a quantizationproblem, by increasing, for example, the energy of the lamps (FIG. 9d).It results from this combination of technical means that the restitutionappliance is able to restitute images, especially characters, withless-blurred details.

Application of the Invention to Cost Reduction

[0221] Cost reduction is defined as a method and system for lowering thecost of an appliance P25 or of an appliance chain P3, especially thecost of the optical system of an appliance or of an appliance chain, themethod consisting in:

[0222] reducing the number of lenses, and/or

[0223] simplifying the shape of the lenses, and/or

[0224] designing an optical system having defects P5 that are largerthan those desired for the appliance or the appliance chain, or choosingthe same from a catalog, and/or

[0225] using materials, components, processing operations ormanufacturing methods that are less costly for the appliance or theappliance chain and that add defects P5.

[0226] The method and system according to the invention can be used tolower the cost of an appliance or of an appliance chain: it is possibleto design a digital optical system, to produce formatted information IFrelated to the defects P5 of the appliance or of the appliance chain, touse this formatted information to enable image-processing means, whetherthey are integrated or not, to modify the quality of images derived fromor addressed to the appliance or to the appliance chain, in such a waythat the combination of the appliance or the appliance chain with theimage-processing means is capable of capturing, modifying or restitutingimages of the desired quality at reduced cost.

1-29. (Canceled).
 30. A method for obtaining a transformed image from adigital image of an appliance chain, the appliance chain containingimage-capture appliances and/or image-restitution appliances, theappliance chain containing at least one appliance; the methodcomprising: automatically determining characteristic data from formattedinformation related to defects of the appliance chain and/or from thedigital image, as characteristic noise data; calculating the transformedimage from the formatted information and from the characteristic noisedata; wherein the transformed image does not exhibit any visible orannoying defect, especially defects related to noise, as regards itssubsequent use.
 31. A method according to claim 30, the method furthercomprising determining the characteristic noise data by: selectinganalysis zones over the digital image, especially as a function of theappliances of the appliance chain and/or of the formatted information,calculating local brightness variations over the analysis zones,deducing the characteristic noise data as a function of a statisticalcalculation of occurrence of the local variations over the set of theanalysis zones.
 32. A method according to claim 31, the method furthercomprising, for deducing the characteristic noise data: constructing ahistogram of occurrences of the local brightness variations, selecting,on the histogram, at least one part of a part situated before a firstlocal maximum, including this maximum; such that the local brightnessvariations related to the noise are then obtained.
 33. A methodaccording to claim 31, the method further comprising, for selection ofanalysis zones over the digital image, classifying the analysis zonesaccording to their mean brightness, to obtain classes; the methodfurther comprising: deducing the characteristic noise data for theanalysis zones belonging to a same class; iterating the deducing foreach of the classes; such that characteristic noise data as a functionof brightness are then obtained.
 34. A method according to claim 30,wherein the formatted information contains the characteristic noisedata.
 35. A method according to claim 30, the method further comprisingemploying a transformation algorithm for constructing an intermediatedigital image; the algorithm having an advantage of making desiredmodifications to the digital image but suffering from a disadvantage ofincreasing the noise of the intermediate digital image.
 36. A methodaccording to claim 39, to calculate a transformed image from theintermediate digital image obtained from the digital image, the methodfurther comprising: employing a function whose purpose is to modifybrightness of the digital image and which has, as arguments, at least:the brightness of a point of the intermediate digital image, thebrightnesses of a zone around the corresponding point of the digitalimage, the characteristic noise data; such that there is then obtained atransformed image exhibiting the desired characteristics and acontrolled noise level.
 37. A method according to claim 36, wherein theintermediate digital image is composed of the digital image.
 38. Amethod according to claim 30, wherein the method calculates atransformed image corrected for all or part of the blurring, the methodfurther comprising: selecting, within the digital image, image zones tobe corrected, constructing, for each image zone to be corrected that hasbeen selected, an enhancement profile based on the formatted informationand on the characteristic noise data, correcting, as a function of theenhancement profile, each image zone to be corrected that has beenselected, to obtain a transformed image zone, combining the transformedimage zones to obtain the transformed image of the digital image; suchthat a deblurred transformed image is then obtained.
 39. A methodaccording to claim 38, wherein the formatted information makes itpossible to determine, for each image zone to be corrected, an imagerepresentation and a reference representation in a base related to theimage zone to be corrected, the method to construct an enhancementprofile from formatted information and noise, the method furthercomprising: determining a profile from the image representation and fromthe reference representation, while taking the noise into account as thecase may be, determining a parameterized operator with which it ispossible to pass from the image representation to the profile; such thatthe set of values of parameters of the parameterized operatorconstitutes the enhancement profile.
 40. A method according to claim 39,the method further comprising, for correction of each image zone to becorrected as a function of the enhancement profile: representing atleast partly, in the base, the image zone to be corrected, applying theparameterized operator to a representation obtained at an end of therepresenting to obtain a corrected representation of the image zone tobe corrected, substituting the representation of the image zone to becorrected by the corrected representation of the image zone to becorrected, so as to obtain a transformed image zone.
 41. A methodaccording to claim 38, the method further comprising: calculating, fromthe transformed image, an image having a controlled noise level, byemploying a function whose purpose is to modify brightness of thedigital image and which has, as arguments, at least: brightness of apoint of the transformed digital image, brightnesses of a zone aroundthe corresponding point of the digital image, characteristic noise data;such that there is then obtained a deblurred image having a controllednoise level.
 42. A method according to claim 30, wherein the formattedinformation depends on values of variable characteristics depending onthe digital image, the method further comprising: determining the valueor values of the variable characteristics for the digital image.
 43. Amethod according to claim 30, the method configured to calculate atransformed image from a digital image and from formatted informationrelated to the defects of an appliance chain containing at least oneimage-restitution appliance, the restitution appliance having a dynamicrange, the transformed image having a dynamic range, the method furthercomprising adapting the dynamic range of the transformed image to thedynamic range of the restitution appliance.
 44. Application to a case ofa digital image composed of color planes, the application comprisingapplying, to each color plane, the method according to claim
 30. 45. Asystem for obtaining a transformed image from a digital image of anappliance chain, the appliance chain containing image-capture appliancesand/or image-restitution appliances, the appliance chain containing atleast one appliance, the system comprising: data-processing means forautomatically determining characteristic data from formatted informationrelated to defects of the appliance chain and/or from the digital imageas characteristic noise data; computer processing means for calculatingthe transformed image from the formatted information and from thecharacteristic noise data; the transformed image does not exhibit anyvisible or annoying defect, especially defects related to noise, asregards its subsequent use.
 46. A system according to claim 45, whereinthe data-processing means for determining the characteristic noise dataincludes: selection means for selecting analysis zones over the digitalimage, especially as a function of the appliances of the appliance chainand/or of the formatted information, calculating means for calculatinglocal brightness variations over the analysis zones, deducing means fordeducing the characteristic noise data as a function of a statisticalcalculation of occurrence of the local variations over the set of theanalysis zones.
 47. A system according to claim 46, the deducing meanscomprising: means for constructing a histogram of occurrences of thelocal brightness variations, selection means for selecting, on thehistogram, at least one part of a part situated before a first localmaximum, including this maximum.
 48. A system according to claim 46, thesystem further comprising: for selection of analysis zones over thedigital image, classification means for classifying the analysis zonesaccording to their mean brightness, to obtain classes; the systemfurther comprising data-processing means for: deducing thecharacteristic noise data for the analysis zones belonging to a sameclass, iterating the deducing for each of the classes.
 49. A systemaccording to claim 45, wherein the formatted information contains thecharacteristic noise data.
 50. A system according to claim 45, thesystem further comprising data-processing means employing atransformation algorithm for constructing an intermediate digital image;the algorithm having an advantage of making the desired modifications tothe digital image but suffering from a disadvantage of increasing thenoise of the intermediate digital image.
 51. A system according to claim50, to calculate a transformed image from the intermediate digital imageobtained from the digital image, the system further comprising:calculating means employing a function whose purpose is to modifybrightness of the digital image and which has, as arguments, at least:the brightness of a point of the intermediate digital image, thebrightnesses of a zone around the corresponding point of the digitalimage, the characteristic noise data.
 52. A system according to claim51, wherein the intermediate digital image is composed of the digitalimage.
 53. A system according to claim 45, wherein the system calculatesa transformed image corrected for all or part of blurring, the systemfurther comprising: selection means for selecting, within the digitalimage, image zones to be corrected, calculating means for constructing,for each image zone to be corrected that has been selected, anenhancement profile based on the formatted information and on thecharacteristic noise data, data-processing means for: correcting, as afunction of the enhancement profile, each image zone to be correctedthat has been selected, to obtain a transformed image zone, and forcombining the transformed image zones to obtain the transformed image ofthe digital image.
 54. A system according to claim 53, wherein theformatted information makes it possible to determine, for each imagezone to be corrected, an image representation and a referencerepresentation in a base related to the image zone to be corrected;wherein in the system the calculating means for constructing anenhancement profile from formatted information and noise furthercomprises means for determining: a profile from the image representationand from the reference representation, while taking the noise intoaccount as the case may be, a parameterized operator with which it ispossible to pass from the image representation to the profile.
 55. Asystem according to claim 54, wherein the data-processing means forcorrection of each image zone to be corrected as a function of theenhancement profile comprising calculating means for: representing atleast partly, in the base, the image zone to be corrected, applying theparameterized operator to the representation of the image zone to becorrected, to obtain a corrected representation of the image zone to becorrected, substituting the representation of the image zone to becorrected by the corrected representation of the image zone to becorrected, so as to obtain a transformed image zone.
 56. A systemaccording to claim 53, further comprising: calculating means forcalculating, from the transformed image, an image having a controllednoise level, by employing a function whose purpose is to modifybrightness of the digital image and which has, as arguments, at least:the brightness of a point of the transformed digital image, thebrightnesses of a zone around the corresponding point of the digitalimage, the characteristic noise data.
 57. A system according to claim45, wherein the formatted information depends on values of variablecharacteristics depending on the digital image, the system furthercomprising: calculating means for determining the value or values of thevariable characteristics for the digital image.
 58. A system accordingto claim 45, to calculate a transformed image from a digital image andfrom formatted information related to the defects of an appliance chaincontaining at least one image-restitution appliance, the restitutionappliance having a dynamic range, the transformed image having a dynamicrange, the system further comprising: data-processing means for adaptingthe dynamic range of the transformed image to the dynamic range of therestitution appliance.